The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第14135号 / 工博第2969号 / 新制||工||1441(附属図書館) / 26441 / UT51-2008-N452 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 杉浦 邦征, 准教授 白土 博通, 准教授 宇都宮 智昭 / 学位規則第4条第1項該当 / Doctor of Engineering / Kyoto University / DFAM

In recent years more emphasis has been placed on in-situ condition based monitoring of engineering systems and structures. Aerospace components are manufactured from composite materials more often. Structural health monitoring (SHM) systems are required in the aerospace industry to monitor the safety and integrity of the structure and will ensure that composites reach its full potential within the industry. Damage detection techniques form an integral part of such SHM systems. With this work a damage detection technique is developed for intended eventual use on composite structures, but starting first on isotropic structures. The damage mechanism that is of interest is delamination damage in composites. A simple numerical equivalent is implemented here however. Two damage indicators, the strain cumulative damage factor (SCDF) and the strain-frequency damage level (SFDL) are introduced. The respective damage indicators are calculated from output-only strain and acceleration response data. The effectiveness of the system to detect damage in the structure is critically evaluated and compared to other damage detection techniques such as the natural frequency method. The sensitivity to damage and performance of both these indicators is examined numerically by evaluating two deterministic damage cases. The numerical study is enhanced through the use of an updated finite element model. The minimum number of sensors capable of detecting the presence and locate damage spatially is determined from numerical simulations. Monte Carlo type analysis is performed by letting the damaged area vary stochastically and calculating the respective damage indicators. The model updating procedure from measured mobility frequency response functions (FRFs) is described. The application of the technique to real structures is examined experimentally. Two test structures with two different damage scenarios are examined. The spatial location and presence of damage can be established from both the SCDF and SFDL values, respectively. The spatial location obtained from the SCDF values corresponded to the known damage location for both the numerical and experimental study. The SFDL proved to be more sensitive than the natural frequency method and could be used to calculate the level of damage within the structure. / Dissertation (MEng)--University of Pretoria, 2009. / Mechanical and Aeronautical Engineering / unrestricted

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京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21080号 / 工博第4444号 / 新制||工||1691(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Detecting structural damage in civil structures through non-destructive means is a growing field in civil engineering. There are many viable methods, but they can often be time consuming and costly; requiring large amounts of data to be collected. By determining which data are the most optimal at detecting damage and which are not the methods can be better optimized. The objective of this thesis was to adapt an existing method of data optimization, used for damage detection in mechanical engineering applications, for use with civil structures. The existing method creates Parameter Signatures based on characteristics from the system being analyzed, from which preferred locations for recording data are determined. For civil structures this method could potentially be used to locate the preferred locations to place accelerometers such that the minimum number of accelerometers is needed to properly detect the location and severity of damage in the structure. This method was first tested on fully analytical computer model structures under perfect conditions to determine its mathematical feasibility with civil structures. It was then tested on data recorded from physical test structures under “real-world” conditions to determine its feasibility as an actual damage detection optimization procedure. Results from the analytical testing show that this is in fact a viable method for determining the preferred sensor positions in civil structures. Furthermore, these results were verified for a variety of excitation types. Physical testing was inconclusive, leading to great insight about what obstacles are impeding this method and should looked at in future research.

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A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data. / En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

<p>  </p> <p>Structural health monitoring is more efficient than traditional visual interval-based structural inspection because structural assessments are implemented when a sensor, such as an accelerometer, measures the vibration of the structure and detects any abnormal readings outside of a safety threshold. These vibrations tend to be atypical when there is damage to the structure. Processing the collected data from an accelerometer using Fast Fourier Transformation (FFT) allows for a graphical visualization of visualizing these atypical measurements in the frequency domain. The comparison and analysis of vibration frequency incurred from three different scenarios (damage, no damage, and impact) in the steel truss prototype has resulted in fundamental knowledge necessary to differentiate an abnormality in accelerometer readings resulting from a vehicular crash against one in which there is actual structural damage. The primary outcome of this work will lead to avoiding unnecessary inspection costs due to possible faulty diagnostics and determining the reliability of the structural health monitoring method.</p>